GENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANS

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dc.contributor.authorMorais, Carlos Henrique Bittencourt
dc.contributor.authorde Moura, Fernanda Marques
dc.contributor.authorAbrahão, Elcio
dc.contributor.authorMaturana, Marcos Coelho
dc.contributor.authorMartins, Marcelo Ramos
dc.contributor.authorde Barros, Leonardo Oliveira
dc.contributor.authorOrlowski, Rene Thiago Capelari
dc.contributor.authorRossi, André Luis Debiaso [UNESP]
dc.contributor.authorSchleder, Adriana Miralles [UNESP]
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionCENPES - Petrobras
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.date.accessioned2023-07-29T13:26:22Z
dc.date.available2023-07-29T13:26:22Z
dc.date.issued2022-01-01
dc.description.abstractCost optimization of asset management is a central issue in the offshore Oil and Gas strategy, and risk-based approaches, such as Risk Based Inspection (RBI), have been more and more employed to assist the segment in the concretization of that goal. The inspection procedure aims mitigating the uncertainty related to the asset degradation state, enabling a better quantification of the actual damage, and, consequently, increases the accuracy of remaining life projections. Since the costs involved in offshore subsea inspections are considerable, inspection plans optimization plays a crucial role in the balance of an asset management program. The present paper discusses the use of genetic algorithms, GA, an optimization technique inspired in the concepts of evolutionary genetics, in the development of inspection plans for subsea equipment. The genes are defined in terms of two variables: the type of inspection to be done and in which period it occurs, considering a finite window of opportunities for inspection occurrences. The optimization process considers a multicriteria objective function, consisting in the dimensions time, cost, and risk. Finally, the application of the proposed methodology is illustrated by means of the elaboration of the inspection plans for a subsea Christmas-tree.en
dc.description.affiliationAnalysis Evaluation and Risk Management Laboratory (LabRisco) Naval Architecture and Ocean Engineering Department University of São Paulo
dc.description.affiliationResearch and Development Center CENPES - Petrobras
dc.description.affiliationInstitute of Science and Engineering (ICE) Sao Paulo State University (Unesp) Evaluation and Risk Management Laboratory (LabRisco), Campus of Itapeva Analysis
dc.description.affiliationUnespInstitute of Science and Engineering (ICE) Sao Paulo State University (Unesp) Evaluation and Risk Management Laboratory (LabRisco), Campus of Itapeva Analysis
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipIdCNPq: 308712/2019-6
dc.identifierhttp://dx.doi.org/10.1115/OMAE2022-78612
dc.identifier.citationProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, v. 2.
dc.identifier.doi10.1115/OMAE2022-78612
dc.identifier.scopus2-s2.0-85140791224
dc.identifier.urihttp://hdl.handle.net/11449/247805
dc.language.isoeng
dc.relation.ispartofProceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
dc.sourceScopus
dc.subjectGenetic Algorithms
dc.subjectMulticriteria Objective Function
dc.subjectNon-Dominated Sorting Genetic Algorithm II (NSGA-II)
dc.subjectOffshore
dc.subjectOil and Gas
dc.subjectRisk Based Inspection (RBI)
dc.titleGENETIC ALGORITHMS APPLIED TO THE CONCEPT OF RISK BASED INSPECTION (RBI): OPTIMIZATION OF INSPECTION PLANSen
dc.typeTrabalho apresentado em evento

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Artigos - Engenharia Industrial Madeireira - Itapeva